SoroushVahidi/scidocs huggingface dataset:
Implementation of multiport dc-dc converter-based Solid State Transformer in smart grid system
(2014)

A solid-state transformer (SST) would be at least as efficient as a conventional version but would provide other benefits as well, particularly as renewable power sources become more widely used. Among its more notable strong points are on-demand reactive power support for the grid, better power quality, current limiting, management of distributed storage devices and a dc bus. Most of the nation's power grid currently operates one way - power flows from the utility to the consumer - and traditional transformers simply change voltage from one level to another. But smart transformers, based on power semiconductor switches, are more versatile. Not only can they change voltage levels, but also can effectively control the power flow in both directions. The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this paper. The DC-DC stage is based on a quad active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A novel technique that complements the SISO controller by taking into account the cross coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models.
Compact Printed Wide-Slot UWB Antenna With 3.5/5.5-GHz Dual Band-Notched Characteristics
(2013)

A novel compact wide-slot ultrawideband (UWB) antenna with dual band-notched characteristics is presented. The antenna consists of an inverted U-shaped slot on the ground plane and a radiation patch similar to the slot that is fed by a 50-Ω microstrip line. By etching a C-shaped slot on the radiation patch and extruding an L-shaped stub from the ground plane, dual band-notched properties in the WiMAX (3.4-3.69 GHz) and WLAN (5.15-5.825 GHz) are achieved. The proposed antenna has a compact size of 20×27 mm2 and operates from 2.89 to 11.52 GHz. Furthermore, nearly omnidirectional radiation patterns and constant gain are obtained in the working band.
Connecting and separating mind-sets: culture as situated cognition.
(2009)

UNLABELLED
People perceive meaningful wholes and later separate out constituent parts (D. Navon, 1977). Yet there are cross-national differences in whether a focal target or integrated whole is first perceived. Rather than construe these differences as fixed, the proposed culture-as-situated-cognition model explains these differences as due to whether a collective or individual mind-set is cued at the moment of observation. Eight studies demonstrated that when cultural mind-set and task demands are congruent, easier tasks are accomplished more quickly and more difficult or time-constrained tasks are accomplished more accurately (


STUDY 1
Koreans, Korean Americans; STUDY 2: Hong Kong Chinese; STUDY 3: European- and Asian-heritage Americans; STUDY 4: Americans;


STUDY
5 Hong Kong Chinese; STUDY 6: Americans; STUDY 7: Norwegians; STUDY 8: African-, European-, and Asian-heritage Americans). Meta-analyses (d = .34) demonstrated homogeneous effects across geographic place (East-West), racial-ethnic group, task, and sensory mode-differences are cued in the moment. Contrast and separation are salient individual mind-set procedures, resulting in focus on a single target or main point. Assimilation and connection are salient collective mind-set procedures, resulting in focus on multiplicity and integration.
Multiview Image Completion with Space Structure Propagation
(2016)

We present a multiview image completion method that provides geometric consistency among different views by propagating space structures. Since a user specifies the region to be completed in one of multiview photographs casually taken in a scene, the proposed method enables us to complete the set of photographs with geometric consistency by creating or removing structures on the specified region. The proposed method incorporates photographs to estimate dense depth maps. We initially complete color as well as depth from a view, and then facilitate two stages of structure propagation and structure-guided completion. Structure propagation optimizes space topology in the scene across photographs, while structure-guide completion enhances, and completes local image structure of both depth and color in multiple photographs with structural coherence by searching nearest neighbor fields in relevant views. We demonstrate the effectiveness of the proposed method in completing multiview images.
Effectiveness and Adoption of a Drawing-to-Learn Study Tool for Recall and Problem Solving: Minute Sketches with Folded Lists
(2017)

Drawing by learners can be an effective way to develop memory and generate visual models for higher-order skills in biology, but students are often reluctant to adopt drawing as a study method. We designed a nonclassroom intervention that instructed introductory biology college students in a drawing method, minute sketches in folded lists (MSFL), and allowed them to self-assess their recall and problem solving, first in a simple recall task involving non-European alphabets and later using unfamiliar biology content. In two preliminary ex situ experiments, students had greater recall on the simple learning task, non-European alphabets with associated phonetic sounds, using MSFL in comparison with a preferred method, visual review (VR). In the intervention, students studying using MSFL and VR had ∼50-80% greater recall of content studied with MSFL and, in a subset of trials, better performance on problem-solving tasks on biology content. Eight months after beginning the intervention, participants had shifted self-reported use of drawing from 2% to 20% of study time. For a small subset of participants, MSFL had become a preferred study method, and 70% of participants reported continued use of MSFL. This brief, low-cost intervention resulted in enduring changes in study behavior.
A Temporal Sequence Learning for Action Recognition and Prediction
(2018)

In this work1, we present a method to represent a video with a sequence of words, and learn the temporal sequencing of such words as the key information for predicting and recognizing human actions. We leverage core concepts from the Natural Language Processing (NLP) literature used in sentence classification to solve the problems of action prediction and action recognition. Each frame is converted into a word that is represented as a vector using the Bag of Visual Words (BoW) encoding method. The words are then combined into a sentence to represent the video, as a sentence. The sequence of words in different actions are learned with a simple but effective Temporal Convolutional Neural Network (T-CNN) that captures the temporal sequencing of information in a video sentence. We demonstrate that a key characteristic of the proposed method is its low-latency, i.e. its ability to predict an action accurately with a partial sequence (sentence). Experiments on two datasets, UCF101 and HMDB51 show that the method on average reaches 95% of its accuracy within half the video frames. Results, also demonstrate that our method achieves compatible state-of-the-art performance in action recognition (i.e. at the completion of the sentence) in addition to action prediction.
Deep Memory Networks for Attitude Identification
(2017)

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral.
 Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
(2017)

Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.
Good features to track
(1994)

No feature-based vision system can work unless good features can be identi ed and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under a ne image transformations. We test performance with several simulations and experiments.
Planning and Acting in Partially Observable Stochastic Domains
(1998)

In this paper we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains We begin by introducing the theory of Markov decision processes mdps and partially observable mdps pomdps We then outline a novel algorithm for solving pomdps o line and show how in some cases a nite memory controller can be extracted from the solution to a pomdp We conclude with a discussion of how our approach relates to previous work the complexity of nding exact solutions to pomdps and of some possibilities for nding approximate solutions
Simultaneous Mosaicing and Tracking with an Event Camera
(2014)

An event camera is a silicon retina which outputs not a sequence of video frames like a standard camera, but a stream of asynchronous spikes, each with pixel location, sign and precise timing, indicating when individual pixels record a threshold log intensity change. By encoding only image change, it offers the potential to transmit the information in a standard video but at vastly reduced bitrate, and with huge added advantages of very high dynamic range and temporal resolution. However, event data calls for new algorithms, and in particular we believe that algorithms which incrementally estimate global scene models are best placed to take full advantages of its properties. Here, we show for the first time that an event stream, with no additional sensing, can be used to track accurate camera rotation while building a persistent and high quality mosaic of a scene which is super-resolution accurate and has high dynamic range. Our method involves parallel camera rotation tracking and template reconstruction from estimated gradients, both operating on an event-by-event basis and based on probabilistic filtering.
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling
(2014)

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
Aerial Lidar Data Classification using AdaBoost
(2007)

We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important in extending the results from one geographic region to another.
Bio-inspired design and dynamic maneuverability of a minimally actuated six-legged robot
(2010)

Rapidly running arthropods like cockroaches make use of passive dynamics to achieve remarkable locomotion performance with regard to stability, speed, and maneuverability. In this work, we take inspiration from these organisms to design, fabricate, and control a 10cm, 24 gram underactuated hexapedal robot capable of running at 14 body lengths per second and performing dynamic turning maneuvers. Our design relies on parallel kinematic mechanisms fabricated using the scaled smart composite microstructures (SCM) process and viscoelastic polymer legs with tunable stiffness. In addition to the novel robot design, we present experimental validation of the lateral leg spring (LLS) locomotion model's prediction that dynamic turning can be achieved by modulating leg stiffness. Finally, we present and validate a leg design for active stiffness control using shape memory alloy and demonstrate the ability of the robot to execute near-gymnastic 90° turns in the span of five strides.
C
1-Almost Periodic Solutions of BAM Neural Networks with Time-Varying Delays on Time Scales
(2015)

On a new type of almost periodic time scales, a class of BAM neural networks is considered. By employing a fixed point theorem and differential inequality techniques, some sufficient conditions ensuring the existence and global exponential stability of C1-almost periodic solutions for this class of networks with time-varying delays are established. Two examples are given to show the effectiveness of the proposed method and results.
Understanding IEEE 1451-Networked smart transducer interface standard - What is a smart transducer?
(2008)

This article introduces the IEEE 1451 standard for networked smart transducers. It discusses the concepts of smart transducers, IEEE 1451 smart transducers, the architecture of the IEEE 1451 family of standards, application of IEEE 1451, and example implementations of the IEEE 1451 standards. In conclusion, the IEEE 1451 suite of standards provides a set of standard interfaces for networked smart transducers, helping to achieve sensor plug and play and interoperability for industry and government.
The Past, Present, and Future of Silicon Photonics
(2006)

The pace of the development of silicon photonics has quickened since 2004 due to investment by industry and government. Commercial state-of-the-art CMOS silicon-on-insulator (SOI) foundries are now being utilized in a crucial test of 1.55-mum monolithic optoelectronic (OE) integration, a test sponsored by the Defense Advanced Research Projects Agency (DARPA). The preliminary results indicate that the silicon photonics are truly CMOS compatible. R&D groups have now developed 10-100-Gb/s electro-optic modulators, ultrafast Ge-on-Si photodetectors, efficient fiber-to-waveguide couplers, and Si Raman lasers. Electrically pumped silicon lasers are under intense investigation, with several approaches being tried; however, lasing has not yet been attained. The new paradigm for the Si-based photonic and optoelectric integrated circuits is that these chip-scale networks, when suitably designed, will operate at a wavelength anywhere within the broad spectral range of 1.2-100 mum, with cryocooling needed in some cases
From Facial Expression Recognition to Interpersonal Relation Prediction
(2017)

Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.
The subepithelial connective tissue graft palatal donor site: anatomic considerations for surgeons.
(1996)

Surgeons must become completely familiar with the anatomy of the palatal donor site to feel confident in providing the subepithelial connective tissue graft procedure. Variations in the size and shape of the hard palate affect the dimensions of donor tissue harvested, as well as the location of the greater palatine neurovascular bundle. This article classifies palatal vaults according to height as high, average, and shallow. Illustrations and cadaver dissection are utilized to demonstrate that surgeons can gain substantial donor tissue specimens without encountering the neurovascular bundle. Actions to be followed in the unlikely event that the neurovasculature is encountered are reviewed.
The biology of facial fillers.
(2009)

The biologic behavior of a facial filler determines its advantages and disadvantages. The purpose of this article is to look at the relevant biology as part of a logical basis for making treatment decisions. Historical perspectives and biologic characteristics such as local tissue reaction (including phagocytosis and granulomatous inflammation) cross-linking, particle concentration, immunogenicity, biofilm formation, gel hardness, and collagen neogenesis are considered. Bovine collagen is the most immunogenic facial filler. Porcine and bioengineered human collagen implants have very low immunogenicity, but allergic reactions and elevations of IgG are possible. Cross-linking and concentration affect the longevity of collagen and hyaluronic acid fillers. Gel hardness affects how a hyaluronic acid filler flows through the syringe and needle. Calcium hydroxylapatite, poly-L-lactic acid, and polymethylmethacrylate fillers have been shown to stimulate collagen neogenesis. It appears that any facial filler can form a granuloma. Bacterial biofilms may play a role in the activation of quiescent granulomas. Various authors interpret the definition and significance of a granuloma differently.
Robust entropy-based endpoint detection for speech recognition in noisy environments
(1998)

This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the speech segments accurately. Experimental results show that this algorithm outperforms the energy-based algorithms in both detection accuracy and recognition performance under noisy environments, with an average error rate reduction of more than 16%.
Customer-Centric Strategic Planning: Integrating CRM in Online Business Systems
(2003)

Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
(2017)

In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers which are easy to derive, but in practice challenging to apply, especially for tasks with many items. For the simple matrix factorization model, an efficient coordinate descent (CD) solver has been previously proposed. However, efficient CD approaches have not been derived for more complex models. In this paper, we provide a new framework for deriving efficient CD algorithms for complex recommender models. We identify and introduce the property of k-separable models. We show that k-separability is a sufficient property to allow efficient optimization of implicit recommender problems with CD. We illustrate this framework on a variety of state-of-the-art models including factorization machines and Tucker decomposition. To summarize, our work provides the theory and building blocks to derive efficient implicit CD algorithms for complex recommender models.